Sunil Kumar Singla

Bio: Sunil Kumar Singla is an academic researcher from Thapar University. The author has contributed to research in topics: PID controller & Biometrics. The author has an hindex of 8, co-authored 40 publications receiving 223 citations.

Design of Fractional Order PID Controller for Load Frequency Control in Perturbed Two Area Interconnected System

Abstract: The vital concern of the load frequency control in an interconnected system is to minimize the frequency and tie-line scheduled power variations. A robust controller is required to overcome these c...

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

Abstract: In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

Prevalence of coronary risk factors in type 2 diabetics without manifestations of overt coronary heart disease.

Abstract: Aims : To find the prevalence of cardiovascular risk factors in type-2 diabetics without manifestations of overt coronary heart disease and to estimate the prevalence of silent myocardial ischaemia in these patients. Methods : Seventy seven patients of type 2 diabetes were recruited for the study (one patient lost after recruitment; 76 completed the study). History and physical examination were recorded. Laboratory investigations included fasting and 2-hour post-prandial blood sugar, blood urea, serum creatinine, lipid profile, glycated haemoglobin, and microalbuminuria. Ultrasonographic scanning of the carotid arteries was performed to measure the carotid IMT. For identification of cases of silent ischaemia, treadmill test (TMT) was performed. Results : The study group was divided into a non-CAD group (n=54), and a silent CAD group (n=22). Twenty-two diabetics were found to have silent CAD as evidenced by a positive TMT result (28.9%). The prevalence of silent myocardial ischaemia was almost similar in both males and females. Serum LDL levels more than 140 mg% had a significant correlation with the prevalence of silent CAD (p=0.04). The difference in CCA-IMT values was found to be statistically significant between the silent CAD and non-CAD groups (p=0.019). Conclusion : High LDL level and greater carotid intima-media thickness are particularly important parameters that can predict if a patient of type 2 diabetes is at risk for silent ischaemia. A high carotid IMT is a surrogate and reliable marker of higher risk of CAD amongst type 2 diabetic patients, even in those without overt CAD. The study also underlines the utility of carotid IMT as a simple, non-invasive, safe, and cheap screening test for the assessment of risk of CAD in type 2 diabetics. ©

ECG Biometric Recognition: A Comparative Analysis

Abstract: The electrocardiogram (ECG) is an emerging biometric modality that has seen about 13 years of development in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our inhouse database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG-based biometrics perform well when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.

Individual identification via electrocardiogram analysis

Abstract: Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.

The Use of Multivariate EMD and CCA for Denoising Muscle Artifacts From Few-Channel EEG Recordings

Abstract: Electroencephalography (EEG) recordings are often contaminated by muscle artifacts. In the literature, a number of methods have been proposed to deal with this problem. Yet most denoising muscle artifact methods are designed for either single-channel EEG or hospital-based, high-density multichannel recordings, not the few-channel scenario seen in most ambulatory EEG instruments. In this paper, we propose utilizing interchannel dependence information seen in the few-channel situation by combining multivariate empirical mode decomposition and canonical correlation analysis (MEMD-CCA). The proposed method, called MEMD-CCA, first utilizes MEMD to jointly decompose the few-channel EEG recordings into multivariate intrinsic mode functions (IMFs). Then, CCA is applied to further decompose the reorganized multivariate IMFs into the underlying sources. Reconstructing the data using only artifact-free sources leads to artifact-attenuated EEG. We evaluated the performance of the proposed method through simulated, semisimulated, and real data. The results demonstrate that the proposed method is a promising tool for muscle artifact removal in the few-channel setting.